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桥梁几何连贯的文本到3D生成与多视图扩散先验和高斯斯点
Feng Yang1, Wenliang Qian1, Wangmeng Zuo2
1Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin 150090, China.
概括
结合得分蒸 (CSD) 通过结合多视图相关性,提高几何一致性和减少3D模型中的工件来增强文本到3D的生成.
科学领域:
- 计算机视觉 计算机视觉
- 3D 图形 3D 图形
- 人工智能的人工智能
背景情况:
- 评分蒸采样 (SDS) 推进了使用2D扩散模型的文本到3D生成.
- 现有的SDS方法忽略了多视图相关性,导致几何不一致性和工件.
研究的目的:
- 引入结合得分蒸 (CSD) 进行几何一致的3D生成.
- 启用稳定和直接优化3D高斯分片 (3D-GS).
主要方法:
- 重构优化作为一个多视图联合优化问题.
- 导出基于梯度的更新规则,将多视图先验合起来,以获得多种视角指导.
- 从随机初始化直接优化3D-GS,并使用可变形四面体网格进行改进.
主要成果:
- 在3D内容中,CSD实现了卓越的几何和语义一致性.
- 与现有方法相比,显示了增强的优化稳定性和多样性.
- 产生高质量的3D内容,提高一致性.
结论:
- 中心证券交易所有效地解决了SDS中多视图相关性忽视的问题.
- 拟议的框架可以实现稳定,直接和高质量的3D生成.
- 中央数据库提供了一个强大的解决方案,用于一致和多样化的文本到3D合成.
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